Context 1. Make Insight-informed Decisions: Clinic collected data on heart disease diagnosis and other patient information, and wants to use the data to make insight-informed decisions. Objective 2. Predict Patient’s Well-being: To identify the rules that will predict whether a patient will have heart disease in the future, based on the data collected on him/her. Strategy 3. Deploy Decision Tree Model: Create a Decision Tree Model, with rules, to predict whether a patient will have a heart disease in the future based on collected data. 3.1 To train and evaluate the model 3.2 Boost the model’s performance 3.3 Conduct predictions Author: Anthony Mok Date: 18 Nov 2023 Email: xxiaohao@yahoo.com

1. Decision Tree
Modelling With
Orange
Identify Rules that Predict
Patient’s Heart Disease
Author: Anthony Mok
Date: 18 Nov 2023
Email: xxiaohao@yahoo.com

2. Characteristics of Orange
Visual programming makes data
mining accessible to a broader
audience
Provides comprehensive data
preprocessing tools
A vast collection of machine learning
algorithms is available
Excels in interactive data visualisation
Scalable, and integrates with external
software packages
An open-source project with a vibrant
community

3. Project’s Context, Objective & Strategies
Make Insight-informed Decisions
Clinic collected data on heart
disease diagnosis and other
patient information, and wants to
use the data to make insight-
informed decisions
Predict Patient’s Well-being
To identify the rules that will
predict whether a patient will have
heart disease in the future, based
on the data collected on him/her
Deploy Decision Tree Model
Create a Decision Tree Model, with
rules, to predict whether a patient
will have a heart disease in the future
based on collected data
To train and evaluate the model
Boost the model’s performance
Conduct predictions

4. Exploratory Data Analysis (EDA)
Findings
Target = Heart Disease
This is a categorical variable,
which has a limited number of
possible values; making it easier
to predict than a continuous
variable, like blood pressure or
cholesterol level
Feature Columns = 9
Row Instances = 918
Blanks & Outliers = None

5. Decision Tree Workflow in Orange

6. Loading File, Selecting Columns & Splitting Data
Loading File
Medical.csv file was loaded into workflow with
‘Gender’, ‘FastingBS’ & ‘Exercise’ classified as
‘categorical’ data & given ‘feature’ role, and
‘HeartDisease’ classified as categorical data
&given the ‘target’ role in the ‘File’ Widget
Selecting Columns
In the ‘Select Column’ Widget,
all feature columns were posted
into the ‘Features’ box. The
‘HeartDisease’, which is the ‘target’ was
clicked into the ‘Target’ box in this widget
Splitting Data
Dataset divided into 70% for
training the model while
keeping the remaining
30% for testing the model

7. Initial Evaluation of Decision Tree Model
Evaluation of Model (30%)
Classification Accuracy for this
model, trained on 30% of the
dataset, is 76.4%
Tree Depth Limited to 10
For initial assessment of the
performance of the Decision Tree
Model, in the Tree Widget, the
maximal tree depth was limited to 10
Evaluation of Model (70%)
Classification Accuracy for this
model, trained on 70% of the
dataset, is 97.1%
Findings
At the Tree Depth of 10, the model
displayed a difference of 15%
when fed with training & testing dataset
Conclusion
Suggests that the Decision
Tree Model Has Been
Overfitted to the training data
Follow-up
To tune the hyperparameters of
the model to enable it to
generalise better to perform well
with the testing data

8. Tuning the Model to Improve Generalisation
Evaluation of Model (30%)
Classification Accuracy for this
model, trained on 30% of the
dataset, is 80.7%
Tree Depth Now Limited to 3
To tune the model, the maximal tree
depth was adjusted several times.
The depth of 3 was
chosen as Classification
Accuracy scores on training
and testing data are high (about 80%)
while the difference between scores
is negligible (at 1.6%)
Evaluation of Model (70%)
Classification Accuracy for this
model, trained on 70% of the
dataset, is 82.3%

9. Confusion Table: False Positives/Negatives
Tree Depth at 10 Tree Depth at 3
False Negative = 17.8%
False Positive = 27.4%
Patients may become untreatable when their conditions go untreated (for False Negatives) or may
have to pay for unwanted treatments and bare the consequences of unneedful side-effects from
the treatment (for False Positives). So, reducing the number of False Negatives and False Positives
in the model is beneficial
False Negative = 19.1%
False Positive = 19.4%
While False Negatives have increased by 1.3%, False
Positives have dropped by 8% with the overall model’s
Classification Accuracy improved by 4.3%

10. Rules Predicting Patient’s Heart Disease*
Sequence of splitting the criteria suggests that Exercise as the top priority
rule with Cholesterol and MaxHR as the two other influencers to
likelihood of Heart Disease in patients
* More details are found in the project report, which are not released at the request of the Clinic

11. Decision Tree
Modelling With
Orange
Identify Rules that Predict
Patient’s Heart Disease
Author: Anthony Mok
Date: 18 Nov 2023
Email: xxiaohao@yahoo.com